Opik

---
name: opik
description: Reference for the Opik SDK — tracing, span types, framework integrations, threads, and the prompt library (Python, TypeScript, REST). Use for "what span types exist", "how do I flush", "track_openai", "add OpikTracer", "version a prompt". To instrument a repo end to end, use the `opik-instrument` skill.
compatibility: Tested with Claude Code; works with any Agent Skills-compatible host (Cursor, VS Code Copilot, Codex). A reference — needs no Opik connection to read; the snippets assume the `opik` Python or TypeScript SDK 2.x. The task-shaped skills (opik-instrument, opik-diagnose, opik-explain, opik-test, opik-compare, opik-evaluate, opik-online-eval, opik-optimize, opik-verify) read this skill's references and expect it installed beside them.
allowed-tools:
  - Read
  - Grep
  - Glob
metadata:
  last_updated: "2026-09-17"
  source_commit: "2.0.0"
---

# Opik SDK Reference

Opik is an open-source LLM observability platform. This skill is a **reference**
for the SDK. To instrument a codebase step by step (detect frameworks, add
config, emit and verify a trace), use the task-shaped `opik-instrument` skill.

## Core concepts

A trace is one execution path (one request → one response). Spans are the
operations inside it and form a hierarchy.

### Span types — the ONLY valid values

| Type | Use for |
|------|---------|
| `general` | orchestration, agent entry points |
| `llm` | model calls |
| `tool` | tools, retrieval, API / DB calls |
| `guardrail` | safety / validation checks |

Do NOT use `retrieval` or any other value.

## Python — tracing

```python
import opik


@opik.track(name="agent", type="general")
def agent(query: str) -> str:
    return generate(retrieve(query))


@opik.track(type="tool")
def retrieve(query): ...


@opik.track(type="llm")
def generate(ctx): ...


opik.flush_tracker()  # required in scripts
```

## TypeScript — tracing

```typescript
import { Opik } from "opik";
const client = new Opik({ projectName: "my-project" });

const trace = client.trace({ name: "agent", input: { query } });
const span = trace.span({ name: "llm-call", type: "llm" });
span.end({ output });
trace.end({ output });
await client.flush();
```

## Framework integrations

Prefer an integration over manual `@opik.track` — integrations capture tokens,
model, and cost automatically. Patterns (full list in
`references/integrations.md`):

- **wrap-the-client** — `track_openai(OpenAI())`, `track_anthropic(...)`
- **global-enable** — `track_crewai(crew=crew)`
- **callback** — `dspy.configure(callbacks=[OpikCallback()])`
- **tracer** — `OpikTracer()` for LangChain / LangGraph / LlamaIndex
- **agent-specific** — `track_adk_agent_recursive(agent, OpikTracer())`

### LiteLLM inside `@opik.track` (common trap)

If code uses `litellm` **and** you add `@opik.track`, pass `current_span_data`
via metadata on every completion call — otherwise `OpikLogger` emits **orphaned**
top-level traces instead of nesting under your span.

```python
from opik.opik_context import get_current_span_data


@opik.track
def call_llm(messages):
    return litellm.completion(
        model="gpt-4o",
        messages=messages,
        metadata={"opik": {"current_span_data": get_current_span_data()}},
    )
```

## Threads (conversations)

Group turns with `thread_id` — one turn = one trace, shared `thread_id` = one
thread. Use for chat / multi-turn; skip for single-shot.

```python
@opik.track(entrypoint=True)
def handle(session_id: str, message: str) -> str:
    opik.update_current_trace(thread_id=session_id)
    return reply(message)
```

## Prompt library

Version prompts with `client.get_prompt` / `create_prompt` (chat variants:
`get_chat_prompt` / `create_chat_prompt`). Store model + temperature in the
prompt `metadata` so they version with the text. Call `get_prompt` **inside** a
`@opik.track` function so the version links to the trace.

```python
@opik.track(entrypoint=True)
def run(question: str) -> str:
    p = client.get_prompt(name="system") or client.create_prompt(
        name="system",
        prompt="You help with {{product}}.",
        metadata={"model": "gpt-4o", "temperature": 0.7},
    )
    return llm(p.format(product="Opik"), model=p.metadata["model"])
```

## How a project is doing

With the MCP connected, start at the project, not at its traces:

```
read("project", "<project name or id>")
```

One call returns the last 7 days against the 7 before — trace count, error
rate, average duration, total cost, SDK traffic only, which is what the Logs
page's four cards show — plus the score names and usage keys the project
actually records, and the freshest experiment, dataset, prompt version and
optimization run in it. `since`/`until` pick another window; `since="30d"` is
what the UI opens on. A rate or an average over a window with no traces comes
back `null` rather than `0`, because a rate over no samples is undefined and
"0% errors" is advice someone may act on.

Then attribute the change rather than restating it:

```
list("project_metric", project_name="<project>", metric_type="trace_cost")
list("project_metric", project_name="<project>", metric_type="span_count",
     breakdown="model", since="30d")
```

Rows are time buckets, not records — `interval` is `hourly`/`daily`/`weekly`/
`total`, and `page`/`size`/`sort` do not apply. `schema("list.project_metric")`
is the metric list, what each is about, and which groupings each accepts; seven
of them accept none. The score names the overview returned are the ones worth
filtering on, and `list("score_name", project_name=…)` has the rest.

## Searching traces

One filter grammar, OQL, serves both the hosted MCP's `list` tool and the
SDK's `search_traces` / `search_spans` / `search_threads`:

```
<field>[.<key>] <op> <value> [AND ...]
ops: = != > >= < <= contains not_contains starts_with ends_with is_empty is_not_empty in not_in
```

Strings in double quotes, numbers bare, `duration` in **milliseconds**, dates as
ISO-8601 instants with a timezone (`"2026-09-08T10:00:00Z"`). Scores and
dictionaries take a key: `feedback_scores.accuracy < 0.5`,
`metadata.environment = "prod"`. `AND` is the only connector.

```
error_info is_not_empty AND duration > 5000
type = "llm" AND usage.total_tokens > 10000            # spans
feedback_scores.hallucination > 0.5 AND start_time >= "2026-09-08T00:00:00Z"
```

With the MCP connected, prefer `list` — it also sorts (`sort="duration desc"`),
windows (`since="1h"`, `"7d"`), and searches free text (`search="order-42"`):

```
list(entity_type="trace", project_name="<project>", since="1h",
     filters="error_info is_not_empty", sort="duration desc")
```

Trace, span and thread lists add `source = "sdk"` unless you name `source`, so
evaluator, playground and experiment traces stay out of the way. A rejected
filter comes back with what fixes it; `schema("list.trace")` (or `list.span`,
`list.thread`, `list.experiment`) is the full field and operator reference.

Without the MCP, the same string goes to the SDK:

```python
client.search_traces(project_name="<project>", filter_string="error_info is_not_empty")
```

## Anti-patterns

| Anti-pattern | Fix |
|--------------|-----|
| span type `retrieval` / custom | use `tool` (or `general`) |
| `get_prompt` outside `@opik.track` | fetch inside — else no trace link |
| deprecated `opik.Prompt` / `opik.Config` | use `client.get_prompt` / config file |
| `litellm` without `current_span_data` | pass it — else orphaned traces |
| no flush in scripts | `opik.flush_tracker()` / `await client.flush()` |

## References

| Topic | File |
|-------|------|
| Python SDK (async, distributed, context) | `references/tracing-python.md` |
| TypeScript SDK | `references/tracing-typescript.md` |
| REST API | `references/tracing-rest-api.md` |
| All integrations | `references/integrations.md` |
| Core concepts (traces, spans, threads) | `references/observability.md` |
| Best practices (lifecycle, monitoring, anti-patterns) | `references/best-practices.md` |
| Agent architecture, reliability, security | `references/agent-patterns.md` |
| Production monitoring, alerts, guardrails | `references/production.md` |
| Evaluation datasets & test suites (reference) | `references/evaluation-datasets.md`, `references/evaluation-test-suites.md` |

To build and run an evaluation, use the `opik-evaluate` skill. For repo instrumentation and config, use the `opik-instrument` skill.